update model card README.md
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README.md
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datasets:
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- food101
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metrics:
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- accuracy
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model-index:
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- name: my_awesome_food_model
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split: train[:5000]
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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datasets:
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- food101
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: my_awesome_food_model
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split: train[:5000]
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args: default
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metrics:
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- name: Precision
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type: precision
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value: 0.8855567868882221
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- name: Recall
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type: recall
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value: 0.887
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- name: F1
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type: f1
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value: 0.8818977914615195
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- name: Accuracy
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type: accuracy
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value: 0.887
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6405
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- Precision: 0.8856
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- Recall: 0.887
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- F1: 0.8819
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- Accuracy: 0.887
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 2.7494 | 0.99 | 62 | 2.5554 | 0.7488 | 0.829 | 0.7859 | 0.829 |
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| 1.9011 | 2.0 | 125 | 1.8058 | 0.8825 | 0.878 | 0.8645 | 0.878 |
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| 1.6532 | 2.98 | 186 | 1.6405 | 0.8856 | 0.887 | 0.8819 | 0.887 |
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### Framework versions
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